NDLI: Human-Robot Mutual Adaptation in Shared Autonomy

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Human-Robot Mutual Adaptation in Shared Autonomy

Content Provider	ACM Digital Library
Author	Zhu, Yu Xiang Srinivasa, Siddhartha Nikolaidis, Stefanos Hsu, David
Abstract	Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference.
Starting Page	294
Ending Page	302
Page Count	9
File Format	PDF
ISBN	9781450343367
DOI	10.1145/2909824.3020252
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-03-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Shared autonomy Human-robot mutual adaptation Planning under uncertainty
Content Type	Text
Resource Type	Article

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